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AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning and self-correction, not only based on the pre-feeded data,bjt it can analyse on its own. Particular applications of AI include expert systems, speech recognition and machine vision.

AI can perform tasks such as identifying patterns in the data more efficiently than humans, enabling businesses to gain more insight out of their data.

To add, Sophia is a social robot developed by Hong Kong-based company Hanson Robotics.

To talk about AI in business world, here are its applications -

*AI in finance AI applied to personal finance applications, such as up, is upending financial institutions. Applications such as these could collect personal data and provide financial advice. Other programs, IBM Watson being one, have been applied to the process of buying a home. Today, software performs much of the trading on Wall Street.

*AI in law The discovery process, sifting through of documents, in law is often overwhelming for humans. Automating this process is a better use of time and a more efficient process. Startups are also building question-and-answer computer assistants that can sift programmed-to-answer questions by examining the taxonomy.

*AI in business Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers.

Artificial intelligence is the latest trend in financial technologies. Both small startups and large institutions apply AI in order to provide faster and safer services for their clients.

*AI in marketing There are plenty of ways that machine learning algorithms can help marketers do their jobs. The goal of marketing is to boost sales and to launch profit-generating campaigns. This requires making decisions based on huge amounts of business data .

*AI In real estate It takes some time till buyers meet agents and tell them what kind of property they want. Artificial intelligence is able to help agents better understand their clients’ needs by means of smart assistant.

*AI in Audit It can be used to devise an accounting fraud-prediction model to more accurately calculate the probability of future material misstatements, improving audit quality.

Review from big 4

EY starts small and aims to demonstrate immediate ROI. Chris Mazzei, chief analytics officer and emerging technology leader at EY, explains, “You have to take it from a business value perspective first, rather than a tech perspective first.”

At Deloitte, a 70-member internal corporate innovation team focuses on all aspects of emerging technology; the team devotes 80% of its time to AI.

PwC, especially in its client services business, favors the use of four-week-long AI “sprints” that mirror the Agile software development method. The goal is to quickly demonstrate a working model for a client before refining it to a higher accuracy.

For KPMG, instead of sampling a snapshot of data, as has long been industry practice, auditors intend to use machine learning to analyse entire populations of data to illuminate apparent anomalies.

Future of AI from experts

Tesla’s founder Elon Musk predicts that humans will be able to merge with robots in the future; Alibaba chairman Jack Ma thinks that in 30 years artificial intelligence will replace CEOs; and Facebook’s creator Mark Zuckerberg envisions that robots will outperform even people’s senses.

Big tech companies such as Google, Microsoft, and Amazon have amassed strong rosters of AI talent and impressive arrays of computers to bolster their core businesses of targeting ads or anticipating your next purchase.

They’ve also begun trying to make money by inviting others to run AI projects on their networks, which will help propel advances in areas such as health care or national security. Improvements to AI hardware, growth in training courses in machine learning, and open source machine-learning projects will also accelerate the spread of AI into other industries.